We discuss efﬁcient Monte Carlo (MC) methods for the estimation of convex risk measures within the portfolio credit risk model CreditMetrics. Our focus lies on the Utilitybased Shortfall Risk (SR) measures, as these avoid several deﬁciencies of the current industry standard Value-at-Risk (VaR). It is demonstrated that the importance sampling method exponential twisting provides computationally efﬁcient SR estimators. Numerical simulations of test portfolios illustrate the good performance of the proposed algorithms.